Thomas Pascoe worked in both the Lloyd's of London insurance market and in corporate finance before joining the Telegraph. He writes about the financial markets. His email is thomas.pascoe@telegraph.co.uk and his Twitter address is @PascoeTelegraph

How to lose $440m in the time it takes to eat lunch

One of the most deadly ingredients of the potion for financial chaos brewed up in the years leading to 2007 was an inability to price risk. High-risk securities were labelled AAA and accounted for accordingly. We are on the verge of making a similar mistake with our trading systems. Algorithmic trading, brought in to minimise human error, has the potential to bring more chaos to the market than even a trader as bloated and incompetent as J P Morgan’s London Whale.

A brief recap on events in the financial world yesterday for those of you, like me, suddenly absorbed by the intricacies of canoe slalom doubles. In Europe, the markets had worked themselves into a state of eager anticipation ahead of a talk from Mario Draghi on how he would solve the Euro crisis. Draghi mumbled a little and kicked the can down the road. The markets panicked and sold off a random selection of securities. If you missed it, don’t worry, this long-running and popular farce will be staging repeat performances at venues across Europe for the remainder of the summer.

In Britain, the Bank of England, having hit upon a formula which does not remedy inflation or stagnant growth, decided to keep with the programme and left rates and printing unchanged. In America, most excitingly of all, a firm named Knight Capital saw its shares fall 63pc in the first full day of trading since it revealed that it had lost almost half a billion dollars in less than an hour using High Frequency Trading on Wednesday.

Knight’s losses were attributed to an errant piece of software which had been merrily staking large sums of money on nonsensical trades apparently unobserved. The programme lost the equivalent of $10m a minute for three quarters of an hour before it was shut down. To put that in context, Knight Capital lost $440m against a second quarter profit of just $3.3m and a pre-crash market capitalisation of $1.01bn.

If the losses serve to reopen the debate on High Frequency Trading (conducted by computers using algorithms rather than humans), then this sad story will have served a purpose.

Algorithmic trading is supposed to minimise risk. It relies upon a computer making a very large number of high value trades which aim to benefit from small imperfections in the market mechanism. For instance, let us say that the price of a company and a commodity are very closely linked because the company has cornered the market on the commodity. For whatever reason the price of the commodity drops while the price of the company does not. At this point the computer will step in and start trading on the assumption that the two will shortly recouple – shorting the firm and buying the commodity. When this happens, it locks in a small profit and goes hunting for the next arbitrage opportunity.

While such an arrangement may be successful in minimising the risks attached to each individual trade, it increases systematic risk because it eliminates the role of immediate human oversight. The trading activities of algorithms are watched over by other algorithms. If they start off down the wrong path, then their ability to trade swiftly and with high stakes means that they can do incredible damage before they are brought back under control.

For a human to lose half a billion dollars would take weeks, given the oversight of a human trader by senior managers and compliance. A computer at an insignificant American finance house has just managed it in the space of a lunch break. Put bluntly, if the system at a big bank was to go haywire like the system at Knight Capital, it would cause a level of financial chaos we can barely imagine.

The mis-pricing of risk is one of the big stories of the era in terms of finance. At present banks are piling money into British government bonds on the assumption that they are risk-free. They are no such thing. They are probably free of default risk because the government has shown a willingness to print money and throw it at the bond markets whenever they look jittery.

Being free of default risk is not the same as being risk-free. The risk for anyone buying British gilts comes from currency depreciation. The same process which sees debts being honoured as new money is printed means that the pound will continue to depreciate. If you end up getting your sterling back with a 1pc return in five years, but the currency has lost another 30pc against the world’s major currencies then you are in a very bad position. That is a high probability risk, and it is ignored by institutional investors.

In the same way, risk is now hysterically priced in the mortgage market. Someone earning £25,000 and paying £900 a month to live in a £120,000 property in London is more than capable of meeting a £450 monthly mortgage, and of absorbing rate increases in the future. However, the 20pc deposit requirement places this forever out of reach. Instead of directing money towards the mortgage markets, banks assess it as too high risk and give it to a computer to gamble with.

Both of these situations can be identified and allowed for by an experienced human. Both require more flexibility of thought than is available to an algorithm.

An excessive reaction? Can human guile really overcome the might of the machines? A rather telling footnote to this story was published by CNBC yesterday. After Knight sent out a note advising traders to avoid using its systems, those traders smelt blood. The number of sell orders increased from138 to 93,977. Those traders will all have made their bosses a lot of money. That’s instinct for you: you can’t buy it, and you certainly can’t programme for it.